Plant functional trait change across a warming tundra biomeThe tundra is warming more rapidly than any other biome on Earth, and the potential ramifications are far-reaching because of global feedback effects between vegetation and climate. A better understanding of how environmental factors shape plant structure and function is crucial for predicting the consequences of environmental change for ecosystem functioning. Here we explore the biome-wide relationships between temperature, moisture and seven key plant functional traits both across space and over three decades of warming at 117 tundra locations. Spatial temperature-trait relationships were generally strong but soil moisture had a marked influence on the strength and direction of these relationships, highlighting the potentially important influence of changes in water availability on future trait shifts in tundra plant communities. Community height increased with warming across all sites over the past three decades, but other traits lagged far behind predicted rates of change. Our findings highlight the challenge of using space-for-time substitution to predict the functional consequences of future warming and suggest that functions that are tied closely to plant height will experience the most rapid change. They also reveal the strength with which environmental factors shape biotic communities at the coldest extremes of the planet and will help to improve projections of functional changes in tundra ecosystems with climate warming. Environment-trait relationships across the tundra biomeWe found strong spatial associations between temperature and community height, SLA and LDMC (Fig. 2a, Extended Data Fig. 2 and Supplementary Table 3) across the 117 survey sites. Both height and SLA increased with summer temperature, but the temperaturetrait relationship for SLA was much stronger at wetter than at drier sites. LDMC was negatively related to temperature, and

The meta-ecosystem framework demonstrates the significance of among-ecosystem spatial flows for ecosystem dynamics and has fostered a rich body of theory. The high level of abstraction of the models, however, impedes applications to empirical systems. We argue that further understanding of spatial dynamics in natural systems strongly depends on dense exchanges between field and theory. From empiricists, more and specific quantifications of spatial flows are needed, defined by the major categories of organismal movement (dispersal, foraging, life-cycle, and migration). In parallel, the theoretical framework must account for the distinct spatial scales at which these naturally common spatial flows occur. Integrating all levels of spatial connections among landscape elements will upgrade and unify landscape and meta-ecosystem ecology into a single framework for spatial ecology.

Perennial rivers and streams make a disproportionate contribution to global carbon (C)cycling. However, the contribution of intermittent rivers and ephemeral streams, which

Ecology and evolution unfold in spatially structured communities, where dispersal links dynamics across scales. Because dispersal is multicausal, identifying general drivers remains challenging. In a coordinated distributed experiment spanning organisms from protozoa to vertebrates, we tested whether two fundamental determinants of local dynamics, top-down and bottom-up control, generally explain active dispersal. We show that both factors consistently increased emigration rates and use metacommunity modelling to highlight consequences on local and regional dynamics.

Ecosystems are widely interconnected by spatial flows of material, but the overall importance of these flows relative to local ecosystem functioning remains unclear. Here we provide a quantitative synthesis on spatial flows of carbon connecting ecosystems worldwide. Cross-ecosystem flows range over eight orders of magnitude, bringing between 10−3 and 105 gC m−2 year−1 to recipient ecosystems. Magnitudes are similar to local fluxes in freshwater and benthic ecosystems, but two to three orders of magnitude lower in terrestrial systems, demonstrating different dependencies on spatial flows among ecosystem types. The strong spatial couplings also indicate that ecosystems are vulnerable to alterations of cross-ecosystem flows. Thus, a reconsideration of ecosystem functioning, including a spatial perspective, is urgently needed.

Assessing individual components of biodiversity, such as local or regional taxon richness, and differences in community composition is a long‐standing challenge in ecology. It is especially relevant in spatially structured and diverse ecosystems. Environmental DNA (eDNA) has been suggested as a novel technique to detect taxa and therefore may allow to accurately measure biodiversity. However, we do not yet fully understand the comparability of eDNA‐based assessments to classical morphological approaches. We assessed may‐, stone‐, and caddisfly genera with two contemporary methods, namely eDNA sampling followed by molecular identification and kicknet sampling followed by morphological identification. We sampled 61 sites distributed over a large river network, allowing a comparison of various diversity measures from the catchment to site levels and providing insights into how these measures relate to network properties. We extended our data with historical morphological records of total diversity at the catchment level. At the catchment scale, identification based on eDNA and kicknet samples detected similar proportions of the overall and cumulative historically documented richness (gamma diversity), 42% and 46%, respectively. We detected a good overlap (62%) between genera identified from eDNA and kicknet samples at the regional scale. At the local scale, we found highly congruent values of local taxon richness (alpha diversity) between eDNA and kicknet samples. Richness of eDNA was positively related to discharge, a descriptor of network position, while kicknet was not. Beta diversity, a measure of dissimilarity between sites, was comparable for the two contemporary methods and is driven by species replacement and not by nestedness. Although eDNA approaches are still in their infancy and optimization regarding sampling design and laboratory work is still needed, our results indicate that it can capture different components of diversity, proving its potential utility as a new tool for large sampling campaigns across hitherto understudied complete river catchments.

Uncovering biodiversity as an inherent feature of ecosystems and understanding its effects on ecosystem processes is one of the most central goals of ecology. Studying organisms’ occurrence and biodiversity patterns in natural ecosystems has spurred the discovery of foundational ecological rules, such as the species–area relationship, and is of general scientific interest. Recent global changes add relevance and urgency to understanding the occurrence and diversity of organisms, and their respective roles in ecosystem processes. While information on ecosystem properties and abiotic environmental conditions are now available at unprecedented, highly‐resolved spatial and temporal scales, the most fundamental variable – biodiversity itself – is still often studied in a local perspective, and generally not available at a wide taxonomic breadth, high temporal scale and spatial coverage. This is limiting the capacity and impact of ecology as a field of science. In this forum article, we propose that complete biodiversity assessments should be inclusive across taxonomic and functional groups, across space, and across time to better understand emergent properties, such as ecosystem functioning. We use riverine ecosystems as a case example because they are among the most biodiverse ecosystems worldwide, but are also highly threatened, such that an in‐depth understanding of these systems is critically needed. Furthermore, their inherent spatial structure requires a multiscale perspective and consideration of spatial autocorrelation structures commonly ignored in biodiversity–ecosystem functioning studies. We show how recent methodological advances in environmental DNA (eDNA) provide novel opportunities to uncover broad biodiversity and link it to ecosystem processes, with the potential to revolutionize ecology and biodiversity sciences. We then outline a roadmap for using this technique to assess biodiversity in a complete and inclusive manner. Our proposed approach will help to get an understanding of biodiversity and associated ecosystem processes at spatial scales relevant for landscape ecology and environmental managers.

Motivation The Tundra Trait Team (TTT) database includes field‐based measurements of key traits related to plant form and function at multiple sites across the tundra biome. This dataset can be used to address theoretical questions about plant strategy and trade‐offs, trait–environment relationships and environmental filtering, and trait variation across spatial scales, to validate satellite data, and to inform Earth system model parameters. Main types of variable contained The database contains 91,970 measurements of 18 plant traits. The most frequently measured traits (> 1,000 observations each) include plant height, leaf area, specific leaf area, leaf fresh and dry mass, leaf dry matter content, leaf nitrogen, carbon and phosphorus content, leaf C:N and N:P, seed mass, and stem specific density. Spatial location and grain Measurements were collected in tundra habitats in both the Northern and Southern Hemispheres, including Arctic sites in Alaska, Canada, Greenland, Fennoscandia and Siberia, alpine sites in the European Alps, Colorado Rockies, Caucasus, Ural Mountains, Pyrenees, Australian Alps, and Central Otago Mountains (New Zealand), and sub‐Antarctic Marion Island. More than 99% of observations are georeferenced. Time period and grain All data were collected between 1964 and 2018. A small number of sites have repeated trait measurements at two or more time periods. Major taxa and level of measurement Trait measurements were made on 978 terrestrial vascular plant species growing in tundra habitats. Most observations are on individuals (86%), while the remainder represent plot or site means or maximums per species. Software format csv file and GitHub repository with data cleaning scripts in R; contribution to TRY plant trait database (www.try-db.org) to be included in the next version release.